Abstract
Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.- Anthology ID:
- 2020.findings-emnlp.114
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1277–1285
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.114
- DOI:
- 10.18653/v1/2020.findings-emnlp.114
- Cite (ACL):
- Kung-Hsiang Huang, Mu Yang, and Nanyun Peng. 2020. Biomedical Event Extraction with Hierarchical Knowledge Graphs. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1277–1285, Online. Association for Computational Linguistics.
- Cite (Informal):
- Biomedical Event Extraction with Hierarchical Knowledge Graphs (Huang et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2020.findings-emnlp.114.pdf
- Code
- PlusLabNLP/GEANet-BioMed-Event-Extraction
- Data
- GENIA